An online prediction compensation method for temperature drift of a gyroscope

Abstract

The present application relates to the technical field of gyroscopes, and particularly relates to an online prediction compensation method for temperature drift of a gyroscope. The method comprises the following steps: detecting gyroscope operation data through a sensor to identify a multi-axis motion state of the gyroscope and generate multi-axis motion state data of the gyroscope; analyzing a multi-axis temperature interference torque of the gyroscope according to the multi-axis motion state data of the gyroscope to generate multi-axis temperature interference torque data of the gyroscope; collecting gyroscope environment data through the sensor, detecting temperature drift of the gyroscope based on the gyroscope environment data and the multi-axis temperature interference torque data of the gyroscope, and generating temperature drift data of the gyroscope; and performing temperature compensation processing on the multi-axis motion state data of the gyroscope based on the temperature drift data of the gyroscope, and calibrating a multi-axis attitude and state of the gyroscope to generate multi-axis attitude-state calibration data of the gyroscope. The present application realizes temperature compensation of the gyroscope through prediction of temperature drift of the gyroscope.

Description

Technical Field

[0001] This invention relates to the field of gyroscope technology, and in particular to an online prediction and compensation method for gyroscope temperature drift. Background Technology

[0002] In high-precision navigation, aerospace, inertial measurement, and intelligent control systems, gyroscopes serve as core sensing elements, and their measurement stability and anti-interference capabilities directly affect the system's operational accuracy and safety. However, in practical applications, gyroscopes often operate in environments with drastic temperature changes. These temperature variations can cause stress fluctuations in the gyroscope's internal structure, sensor drift, and abnormal axis response, leading to temperature drift. This results in systematic errors in angular velocity measurements, affecting the accuracy of attitude estimation and navigation trajectory. In existing technologies, drift suppression is achieved through temperature correlation factor adjustment or interpolation, which relies on prior data under specific operating conditions. This makes it difficult to perceive the complex linkage between temperature disturbances and multi-axis motion in real time. In actual operation, gyroscopes are usually under a combined state of high-frequency vibration, multi-axis coordinated rotation, and external stress disturbances. Temperature changes not only affect the measurement output of a single axis, but may also cause problems such as dynamic inconsistency between axes, coupling gain imbalance, or even phase misalignment. Especially during multi-axis unsteady operation, the non-uniformity of thermal stress distribution, the time-varying nature of the deflection center of gravity, and the asymmetry of the bearing component response are more likely to amplify the temperature drift effect. The compensation mechanism has problems such as response lag and poor adaptability, making it difficult to ensure the consistency of stability and accuracy. Summary of the Invention

[0003] Based on this, the present invention provides an online prediction and compensation method for gyroscope temperature drift to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objective, an online prediction and compensation method for gyroscope temperature drift includes the following steps:

[0005] Step S1: Detect gyroscope operating data through sensors, identify gyroscope multi-axis motion state based on gyroscope operating data, and generate gyroscope multi-axis motion state data;

[0006] Step S2: Perform multi-axis temperature disturbance torque analysis on the gyroscope based on the multi-axis motion state data of the gyroscope, and generate multi-axis temperature disturbance torque data of the gyroscope;

[0007] Step S3: Collect gyroscope environmental data through sensors, and perform gyroscope temperature drift detection based on gyroscope environmental data and gyroscope multi-axis temperature disturbance torque data to generate gyroscope temperature drift data.

[0008] Step S4: Perform gyroscope temperature compensation processing on the gyroscope multi-axis motion state data based on the gyroscope temperature drift data to generate temperature-compensated gyroscope multi-axis motion state data.

[0009] Step S5: Perform gyroscope multi-axis attitude and state calibration based on the temperature-compensated gyroscope multi-axis motion state data to generate gyroscope multi-axis attitude-state calibration data.

[0010] Furthermore, step S1 includes the following steps:

[0011] Step S11: Detect gyroscope operating data through sensors, identify gyroscope multi-axis motion trajectory based on gyroscope operating data, and generate gyroscope multi-axis motion trajectory data;

[0012] Step S12: Analyze the multi-axis motion characteristics of the gyroscope based on the multi-axis motion trajectory data of the gyroscope, and generate multi-axis motion characteristic data of the gyroscope;

[0013] Step S13: Perform gyroscope multi-axis angular velocity decomposition and motion trajectory detection based on gyroscope multi-axis motion characteristic data to generate gyroscope multi-axis angular velocity decomposition-motion trajectory data;

[0014] Step S14: Based on the gyroscope multi-axis angular velocity decomposition-motion trajectory data, perform gyroscope multi-axis motion state recognition and generate gyroscope multi-axis motion state data.

[0015] Furthermore, step S2 includes the following steps:

[0016] Step S21: Perform gyroscope multi-axis structure partitioning analysis based on gyroscope multi-axis motion state data to generate gyroscope multi-axis structure partitioning data;

[0017] Step S22: Analyze the gyroscope operation and temperature response characteristic parameters based on the gyroscope multi-axis structure partition data, and generate gyroscope operation-temperature response characteristic parameter data;

[0018] Step S23: Perform multi-axis temperature coupling response analysis of the gyroscope based on the gyroscope operation-temperature response characteristic parameter data, and generate multi-axis temperature response data of the gyroscope;

[0019] Step S24: Perform gyroscope multi-axis temperature disturbance torque analysis based on gyroscope multi-axis temperature response data to generate gyroscope multi-axis temperature disturbance torque data.

[0020] Furthermore, step S23 includes the following steps:

[0021] Step S231: Identify the multi-axis rotation nodes of the gyroscope based on the gyroscope operation-temperature response characteristic parameter data, and generate gyroscope multi-axis rotation node data;

[0022] Step S232: Perform gyroscope multi-axis linkage analysis based on gyroscope multi-axis rotation node data to generate gyroscope multi-axis linkage data;

[0023] Step S233: Analyze the temperature coupling strength of each axis of the gyroscope based on the multi-axis linkage data of the gyroscope, and generate temperature coupling strength data of each axis of the gyroscope.

[0024] Step S234: Perform multi-axis temperature coupling response analysis of the gyroscope based on the temperature coupling strength data of each axis of the gyroscope and the multi-axis linkage data of the gyroscope, and generate multi-axis temperature response data of the gyroscope.

[0025] Furthermore, step S234 includes the following steps:

[0026] Based on the multi-axis linkage data of the gyroscope, the gyroscope deflection center of gravity is identified, and gyroscope deflection center of gravity data is generated.

[0027] Based on the temperature coupling strength data of each axis of the gyroscope and the deflection center of gravity data of the gyroscope, the friction characteristics of the multi-bearing gyroscope are analyzed, and the friction characteristic data of the multi-bearing gyroscope are generated.

[0028] Based on the temperature coupling strength data of each axis of the gyroscope, the friction characteristics data of the multi-bearing of the gyroscope, and the deflection center of gravity data of the gyroscope, the multi-axis temperature coupling response analysis of the gyroscope is performed to generate multi-axis temperature response data of the gyroscope.

[0029] Furthermore, step S24 includes the following steps:

[0030] Step S241: Analyze the multi-axis rotation and temperature change rate of the gyroscope based on the multi-axis temperature response data of the gyroscope, and generate multi-axis rotation-temperature change rate data of the gyroscope.

[0031] Step S242: Based on the gyroscope multi-axis rotation-temperature change rate data, identify the gyroscope temperature-induced multi-axis rotation phase shift and generate gyroscope temperature-induced multi-axis rotation phase shift data;

[0032] Step S243: Perform gyroscope multi-axis temperature disturbance torque analysis based on gyroscope temperature-induced multi-axis rotation phase shift data to generate gyroscope multi-axis temperature disturbance torque data.

[0033] Furthermore, step S3 includes the following steps:

[0034] Step S31: Collect gyroscope environmental data through sensors, extract gyroscope environmental parameter features based on the gyroscope environmental data, and generate gyroscope environmental parameter feature data;

[0035] Step S32: Perform gyroscope multi-axis thermal drift mapping processing based on gyroscope environmental parameter characteristic data and gyroscope multi-axis temperature disturbance torque data to generate gyroscope multi-axis thermal drift mapping data;

[0036] Step S33: Perform gyroscope temperature drift detection based on gyroscope multi-axis thermal drift mapping data to generate gyroscope temperature drift data.

[0037] Furthermore, step S32 includes the following steps:

[0038] Step S321: Analyze the fluctuation relationship of the gyroscope's environmental factors based on the gyroscope's environmental parameter characteristic data, and generate environmental factor fluctuation relationship data;

[0039] Step S322: Analyze the temperature, humidity and electromagnetic noise disturbance intensity of the gyroscope based on the environmental factor fluctuation relationship data, and generate temperature, humidity and electromagnetic noise disturbance intensity data.

[0040] Step S323: Based on the temperature and humidity-electromagnetic noise disturbance intensity data, perform gyroscope axis offset and temperature trend analysis on the gyroscope multi-axis temperature disturbance torque data to generate gyroscope axis offset-temperature trend data;

[0041] Step S324: Analyze the multi-axis thermal stress distribution characteristics of the gyroscope based on the offset-temperature trend data of each axis, and generate multi-axis thermal stress distribution characteristic data of the gyroscope.

[0042] Step S325: Perform gyroscope multi-axis thermal drift mapping processing based on the gyroscope's axis offset-temperature trend data and gyroscope multi-axis thermal stress distribution characteristic data to generate gyroscope multi-axis thermal drift mapping data.

[0043] Furthermore, step S4 includes the following steps:

[0044] Step S41: Perform multi-axis rotation and temperature change analysis of the gyroscope based on the gyroscope temperature drift data to generate multi-axis rotation-temperature change data of the gyroscope;

[0045] Step S42: Based on the multi-axis rotation-temperature change data of the gyroscope, identify the multi-axis temperature-induced dynamic imbalance characteristics of the gyroscope based on the multi-axis motion state data of the gyroscope, and generate multi-axis temperature-induced dynamic imbalance characteristic data of the gyroscope.

[0046] Step S43: Based on the multi-axis temperature-induced dynamic imbalance characteristic data of the gyroscope, perform gyroscope temperature drift influence area location analysis and generate gyroscope temperature drift influence area location data;

[0047] Step S44: Perform gyroscope temperature compensation processing based on the gyroscope temperature drift affected area positioning data and gyroscope multi-axis rotation-temperature change data to generate temperature-compensated gyroscope multi-axis motion state data.

[0048] Furthermore, step S5 includes the following steps:

[0049] Step S51: Perform gyroscope multi-axis attitude drift deviation analysis based on the temperature-compensated gyroscope multi-axis motion state data to generate gyroscope multi-axis attitude drift deviation data.

[0050] Step S52: Evaluate the attitude stability of the gyroscope based on the multi-axis attitude drift deviation data of the gyroscope, and generate gyroscope attitude stability evaluation data;

[0051] Step S53: Based on the gyroscope attitude stability evaluation data, perform gyroscope multi-axis attitude and state calibration on the temperature-compensated gyroscope multi-axis motion state data to generate gyroscope multi-axis attitude-state calibration data.

[0052] The beneficial effects of this invention are:

[0053] This invention proposes an online prediction and compensation method for gyroscope temperature drift. By detecting gyroscope operating data through sensors, it can pinpoint even the smallest motion changes. Based on this data, it identifies the multi-axis motion state of the gyroscope, distinguishing motion characteristics along different axes and clarifying key information such as the amplitude, frequency, and direction of each axis's motion. Analyzing the multi-axis temperature disturbance torque based on the gyroscope's multi-axis motion state data allows for precise location of disturbance torques caused by temperature changes along different axes. By combining the dynamic characteristics of multi-axis motion and considering the mutual influence between axes, it provides a more comprehensive understanding of the distribution and changing trends of temperature disturbance torques. Furthermore, by collecting environmental data from sensors, including temperature, humidity, and air pressure, and using this environmental data along with the multi-axis temperature disturbance torque data for gyroscope temperature drift detection, it can accurately capture the gyroscope's temperature drift characteristics. Combining environmental data with temperature disturbance torque data for analysis allows for a more comprehensive consideration of the combined impact of various factors on temperature drift. This paper describes a gyroscope temperature compensation process for multi-axis motion data. By utilizing temperature drift data, adjustments can be made to the motion data along different axes to eliminate errors caused by temperature variations. Personalized processing is performed based on the drift characteristics of each axis to ensure that the motion data for each axis is restored to its true value. The paper then uses the temperature-compensated multi-axis motion data for gyroscope attitude and state calibration, achieving high-precision operation. Attitude and state calibration optimizes and adjusts the overall performance of the gyroscope. Based on the accurate temperature-compensated data, detailed calibration of the gyroscope's multi-axis attitude and operating state is possible.

[0054] The present invention provides an online prediction and compensation method for gyroscope temperature drift. By sensing and analyzing the complex linkage between temperature disturbance and multi-axis motion state in real time under the combined state of high-frequency vibration, multi-axis coordinated rotation and external stress disturbance of the gyroscope, the method performs timely and targeted temperature compensation for the gyroscope. This solves the problems of dynamic inconsistency between gyroscope axes, imbalance of coupling gain and even phase disorder, as well as non-uniformity of thermal stress distribution. It also enables the gyroscope temperature compensation to adapt to the time-varying nature of the deflection center of gravity and solves the asymmetric amplification of temperature drift effect in the response of bearing components. Attached Figure Description

[0055] Figure 1 This is a schematic flowchart illustrating the steps of an online prediction and compensation method for gyroscope temperature drift according to the present invention.

[0056] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0057] Figure 3 for Figure 2 A detailed flowchart illustrating the implementation steps of step S23.

[0058] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0059] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0060] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0061] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0062] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides an online prediction and compensation method for gyroscope temperature drift, comprising the following steps:

[0063] Step S1: Detect gyroscope operating data through sensors, identify gyroscope multi-axis motion state based on gyroscope operating data, and generate gyroscope multi-axis motion state data;

[0064] Step S2: Perform multi-axis temperature disturbance torque analysis on the gyroscope based on the multi-axis motion state data of the gyroscope, and generate multi-axis temperature disturbance torque data of the gyroscope;

[0065] Step S3: Collect gyroscope environmental data through sensors, and perform gyroscope temperature drift detection based on gyroscope environmental data and gyroscope multi-axis temperature disturbance torque data to generate gyroscope temperature drift data.

[0066] Step S4: Perform gyroscope temperature compensation processing on the gyroscope multi-axis motion state data based on the gyroscope temperature drift data to generate temperature-compensated gyroscope multi-axis motion state data.

[0067] Step S5: Perform gyroscope multi-axis attitude and state calibration based on the temperature-compensated gyroscope multi-axis motion state data to generate gyroscope multi-axis attitude-state calibration data.

[0068] In the embodiments of this invention, please refer to Figure 1 The diagram shown is a flowchart illustrating the steps of an online prediction and compensation method for gyroscope temperature drift according to the present invention. In this example, the online prediction and compensation method for gyroscope temperature drift includes the following steps:

[0069] Step S1: Detect gyroscope operating data through sensors, identify gyroscope multi-axis motion state based on gyroscope operating data, and generate gyroscope multi-axis motion state data;

[0070] In this embodiment of the invention, a general-purpose sensor signal control circuit is used as the core detection unit. The circuit's built-in accelerometer (such as a quartz flexural accelerometer) collects real-time X, Y, and Z-axis motion data from the gyroscope, including angular velocity (range ±2000 degrees / s), angular displacement (resolution 0.001 degrees), and vibration amplitude (0-5V analog quantity) every 10ms. The collected raw data is converted into digital signals via an ADC and transmitted to a processor for multi-axis motion trajectory recognition: a sliding window algorithm (window length 100ms) is used to extract trajectory feature points from the continuous data, and the least squares method is used to fit and generate motion trajectory curves for the X-axis (e.g., horizontal rotation), Y-axis (e.g., pitch), and Z-axis (e.g., vertical flip). The trajectory curve is then subjected to Fourier transform to analyze characteristic parameters such as motion frequency (0.1-100Hz) and peak acceleration (0-500 degrees / s²) of each axis. Combined with the Kalman filter algorithm, the angular velocity components are decomposed and the motion trajectory is plotted. Finally, multi-axis motion state data containing real-time angular velocity, angular displacement, motion direction and trajectory curvature of the three axes are generated and stored in the circuit's built-in 128KB FLASH.

[0071] Step S2: Perform multi-axis temperature disturbance torque analysis on the gyroscope based on the multi-axis motion state data of the gyroscope, and generate multi-axis temperature disturbance torque data of the gyroscope;

[0072] In this embodiment of the invention, based on multi-axis motion state data, the FPGA module (Field Programmable Gate Array module, containing 1152 logic units) in the fiber optic gyroscope control circuit is used for structural partitioning analysis. The gyroscope is divided into three physical partitions: a sensing head (including a laser resonant cavity), a driving circuit (including a DAC module), and a detection circuit (including an ADC module). The temperature response data of each partition under motion states of 300 degrees / s on the X-axis, 200 degrees / s on the Y-axis, and 150 degrees / s on the Z-axis (sampling interval 100ms) is recorded by the timing analysis unit built into the FPGA. Thermocouple sensors (accuracy ±0.1 degrees Celsius) are used to collect the temperature of each partition (-55 degrees Celsius to +85 degrees Celsius). Characteristic parameters of temperature change of each partition with motion state are extracted, including the temperature response time of the sensing head (≤500ms), the temperature coefficient of the driving circuit (≤10ppm / degree Celsius), and the thermal hysteresis of the detection circuit (≤2 degrees Celsius). By establishing a three-dimensional thermal coupling model, the XY-axis cross-coupling coefficient (≤5%) and YZ-axis thermal conductivity (0.2 W / (m·K)) were calculated. Combined with Newton's law of cooling, the temperature field distribution of each axis was analyzed, and the quantitative relationship between temperature disturbance torque and motion state was determined: when the X-axis angular velocity increases from 100 degrees / s to 500 degrees / s, the temperature of the sensing head increases by 3 degrees Celsius, and the corresponding disturbance torque increases by 0.002 N·m; when the Z-axis rotates continuously (300 degrees / s), the disturbance torque fluctuates by 0.001 N·m for every 1 degree Celsius increase in temperature of the drive circuit. Finally, a dataset containing the amplitude (0-0.01 N·m) and phase (0-360 degrees) of the three-axis temperature disturbance torque was generated.

[0073] Step S3: Collect gyroscope environmental data through sensors, and perform gyroscope temperature drift detection based on gyroscope environmental data and gyroscope multi-axis temperature disturbance torque data to generate gyroscope temperature drift data.

[0074] In this embodiment of the invention, an environmental acquisition module integrated into a general-purpose computer platform is used. This module includes a temperature sensor (accuracy ±0.3 degrees Celsius), a humidity sensor (0-100%RH, error ±2%), and an electromagnetic interference detector (frequency 10kHz-1GHz, range 0-100dBμV). It acquires environmental data around the gyroscope every 50ms, including operating temperature (-55 degrees Celsius to +85 degrees Celsius), relative humidity (20%-80%), and electromagnetic interference intensity (≤50dBμV). The environmental data and the temperature disturbance torque data generated in step S2 are input into the FPGA logic unit of the circuit and processed through the established three-dimensional mapping model: First, the ambient temperature data is linearly fitted to calculate the change in disturbance torque corresponding to each degree Celsius (e.g., when the temperature rises from 25 degrees Celsius to 85 degrees Celsius, the X-axis disturbance torque increases by 0.005 N·m); then, the heat conduction efficiency is corrected by combining the humidity data (for every 10% increase in humidity, the thermal resistance increases by 5%), and the influence of circuit noise is compensated according to the electromagnetic interference intensity (for every 10 dBμV increase in interference, the data deviation is corrected by 0.001 N·m). The mapping model outputs the temperature drift of each axis of the gyroscope: the X-axis drifts +0.02 degrees / h at -55 degrees Celsius, -0.005 degrees / h at 25 degrees Celsius, and +0.03 degrees / h at 85 degrees Celsius; the Y-axis and Z-axis drift trends are similar but the amplitude difference is ≤0.005 degrees / h, and finally a dataset containing the temperature drift values ​​of the three axes across the entire temperature range (-55 degrees Celsius to +85 degrees Celsius) is generated.

[0075] Step S4: Perform gyroscope temperature compensation processing on the gyroscope multi-axis motion state data based on the gyroscope temperature drift data to generate temperature-compensated gyroscope multi-axis motion state data.

[0076] In this embodiment of the invention, temperature drift data is processed using a temperature compensation unit within an integrated inertial computer system. This unit includes a high-precision DAC (14-bit, 250 MSPS) and FPGA logic resources (350 K-cells). First, the temperature-drift curve is extracted from the drift data, and compensation coefficients for each axis are obtained through polynomial fitting: the X-axis is... Y-axis Z-axis The gyroscope's multi-axis motion data (real-time angular velocity and angular displacement) is input into the compensation unit. Based on the current ambient temperature (detected by a built-in 18B20 sensor, with an accuracy of ±0.3 degrees Celsius), the calculation is performed using a formula... (in For temperature compensation amount, For the current ambient temperature, For compensation coefficient, The compensation amount is calculated based on the motion duration. For example, when the X-axis operates at 50 degrees Celsius for 1 hour, the compensation amount = 50 × 0.0005 × 1 = 0.025 degrees / h, which is used to reverse the original angular velocity data (subtracting the angular velocity component corresponding to 0.025 degrees / h). Simultaneously, real-time filtering is implemented via FPGA, using a 10th-order Butterworth low-pass filter (cutoff frequency 10Hz) to eliminate high-frequency noise during the compensation process, ensuring the smoothness of the corrected data (fluctuation ≤ 0.001 degrees / h). The final temperature-compensated data includes the triaxial corrected angular velocity (error ≤ 0.005 degrees / s) and angular displacement (error ≤ 0.0001 degrees), and is transmitted to the subsequent calibration unit via an RS-422 interface (baud rate 10Mbps).

[0077] Step S5: Perform gyroscope multi-axis attitude and state calibration based on the temperature-compensated gyroscope multi-axis motion state data to generate gyroscope multi-axis attitude-state calibration data.

[0078] In this embodiment of the invention, the PSOC processor in the integrated processor is used to calibrate the temperature-compensated multi-axis motion state data of the gyroscope. The processor receives the compensated three-axis attitude data (every 20ms) and calculates the current attitude angle (roll angle) using the built-in attitude calculation algorithm. Pitch angle Heading angle (resolution 0.01 degrees), and the reference attitude angle (roll angle reference) provided by the high-precision turntable (positioning accuracy ±0.001 degrees). Pitch angle reference Heading Angle Reference Compare and generate deviation values: roll angle deviation Pitch angle deviation Heading angle deviation All units are degrees. Calibration is performed using a PID control algorithm based on the deviation value; the correction amount is calculated using the following formula: ,in For servo motor drive current correction amount, For attitude angle deviation ( , or ), For proportionality coefficient, The integral coefficient is... These are the differential coefficients. For the integral term of deviation, This is the rate of change of the deviation. For example, when When, substituting into the formula, we get The actual drive current is increased by 50mA (converted according to the proportional amplification factor), causing the roll angle to adjust negatively by 0.1 degrees. During calibration, the correction amount is fed back in real time via the 1553B bus, and the calibration parameters are updated every 50ms until the deviation value is ≤0.005 degrees. The final generated multi-axis attitude-state calibration data includes the attitude angles after three-axis calibration ( Correction, Correction, The calibration results (correction, error ≤ 0.005 degrees), servo motor drive current (stable within ±10mA fluctuation), and calibration completion time (≤ 1s) are stored in 256MB NOR FLASH and simultaneously output to an external system via a gigabit Ethernet interface.

[0079] Furthermore, step S1 includes the following steps:

[0080] Step S11: Detect gyroscope operating data through sensors, identify gyroscope multi-axis motion trajectory based on gyroscope operating data, and generate gyroscope multi-axis motion trajectory data;

[0081] In this embodiment of the invention, a general-purpose sensor signal control circuit (including a 14-bit ADC, sampling rate of 250Ksps) is used to connect to the three-axis angular velocity sensor built into the gyroscope (such as the detection unit in a fiber optic gyroscope circuit, with a measurement range of ±300 degrees / s and an accuracy of ±0.01 degrees / s). Real-time angular velocity data of the X, Y, and Z axes are collected every 10ms, and the timestamp of the data acquisition time is recorded synchronously (accuracy of 1μs). The collected raw data is transmitted to the FPGA module (350K logic units) of the DLM20S484SIP circuit via an RS-422 interface (baud rate of 10Mbps). The FPGA preprocesses the data, using a sliding window mean filter (window length of 50ms) to eliminate high-frequency noise and ensure that the data fluctuation amplitude is ≤0.005 degrees / s. Based on the filtered data, the Hough transform algorithm is used to identify the straight and curved segments of the motion trajectory on each axis: when the X-axis exhibits a linear change within the range of 0-100 degrees / s, it is determined to be a straight track; when the Y-axis shows a parabolic change from 150 degrees / s to 200 degrees / s and then back to 150 degrees / s, it is determined to be a curved track; when the Z-axis maintains a constant value of 50 degrees / s, it is determined to be a uniform linear track. Finally, multi-axis motion trajectory data is generated, including the track type (straight / curved), starting coordinates (time-angle), and ending coordinates (time-angle) for each axis.

[0082] Step S12: Analyze the multi-axis motion characteristics of the gyroscope based on the multi-axis motion trajectory data of the gyroscope, and generate multi-axis motion characteristic data of the gyroscope;

[0083] In this embodiment of the invention, motion characteristics are analyzed using a PSOC processor (800MHz clock speed) integrated with an inertial computer, based on multi-axis motion trajectory data. The processor calls the built-in trajectory feature extraction module to calculate the slope (i.e., angular acceleration, in degrees / s²) of the X-axis straight track. For example, if the X-axis accelerates from 0 degrees / s to 100 degrees / s in 2 seconds, the slope is 50 degrees / s². The second derivative of the Y-axis curved track is performed to obtain the rate of change of angular acceleration (e.g., from 10 degrees / s² to 30 degrees / s², the rate of change is 20 degrees / s³). The duration of the Z-axis uniform velocity track is calculated (e.g., maintaining 50 degrees / s for 10 seconds). Simultaneously, the trajectory data of each axis is converted from the time domain to the frequency domain through Fourier transform to extract characteristic frequencies: the main frequency component of the X-axis is 1Hz (corresponding to periodic oscillation), the main frequency component of the Y-axis is 0.5Hz (corresponding to slow deflection), and there is no significant frequency component of the Z-axis (corresponding to static). In addition, the smoothness of each axis track is calculated and determined by the sum of squared differences between adjacent data points. For example, a sum of squared differences for the X-axis ≤ 10 (degrees / s)² is considered high smoothness, while a sum of squared differences for the Y-axis = 50 (degrees / s)² is considered medium smoothness. Finally, multi-axis motion characteristic data containing angular acceleration, frequency components, and smoothness is generated and transmitted to the next processing unit via an SPI interface (10 Mbps).

[0084] Step S13: Perform gyroscope multi-axis angular velocity decomposition and motion trajectory detection based on gyroscope multi-axis motion characteristic data to generate gyroscope multi-axis angular velocity decomposition-motion trajectory data;

[0085] In this embodiment of the invention, based on the multi-axis motion characteristic data of the gyroscope, the DSP module (16-bit precision) of the fiber optic gyroscope control circuit is used to perform angular velocity decomposition. For the X-axis composite motion (including a constant velocity of 30 degrees / s and an acceleration of 20 degrees / s²), the constant velocity component (30 degrees / s) and the acceleration component (20 degrees / s² × time) are separated using the vector decomposition formula; for the Y-axis curvilinear motion (including sinusoidal changes), it is decomposed into a fundamental frequency component (150 degrees / s × sin(πt)) and a DC component (50 degrees / s); for the Z-axis constant motion, the single component of 50 degrees / s is directly retained. After the decomposition is completed, motion trajectory detection is initiated: with the initial time as the origin (0,0), the integral calculation is performed based on the decomposed angular velocity components, and the angle value is accumulated every 1ms (the angle value formula is: ,in For angle, Angular velocity, The following time intervals are used to generate X-axis trajectories (e.g., 0→0.05°→0.2°→...→50°), Y-axis trajectories (e.g., 0→0.1°→0.3°→...→30°), and Z-axis trajectories (e.g., 0→0.05°→0.1°→...→25°). The trajectory equations for each axis are obtained using a trajectory fitting algorithm (least squares method): the X-axis is a linear equation θ=25t, the Y-axis is a quadratic equation θ=5t²+10t, and the Z-axis is a constant equation θ=50t. Finally, multi-axis angular velocity decomposition-motion trajectory data containing angular velocity components (uniform / accelerated), trajectory equation parameters, and real-time angular coordinates is generated and stored in a 32GB EMMC.

[0086] Step S14: Based on the gyroscope multi-axis angular velocity decomposition-motion trajectory data, perform gyroscope multi-axis motion state recognition and generate gyroscope multi-axis motion state data.

[0087] In this embodiment of the invention, motion state recognition is performed using the attitude recognition unit of the integrated processor, based on the multi-axis angular velocity decomposition-motion trajectory data of the gyroscope. The unit has a built-in state determination matrix, which classifies the motion state into five types: stationary (angular velocity of each axis ≤ 0.1 degrees / s), uniform rotation (angular velocity is constant, angular acceleration ≤ 0.5 degrees / s²), accelerating rotation (angular acceleration > 0.5 degrees / s² and direction remains unchanged), decelerating rotation (angular acceleration < 0 and absolute value > 0.5 degrees / s²), and oscillating rotation (angular velocity alternates between positive and negative, period ≤ 10s). For the X-axis: the trajectory equation θ = 25t corresponds to an angular velocity of 25 degrees / s (constant) and an angular acceleration of 0 degrees / s², indicating a uniform rotation state; the Y-axis trajectory equation θ = 5t² + 10t corresponds to an angular velocity of 10 + 10t (increasing with time) and an angular acceleration of 10 degrees / s², indicating an accelerating rotation state; the Z-axis trajectory maintains 50 degrees / s for 10 seconds, then suddenly drops to 0 degrees / s (within 1 second), with an angular acceleration of -50 degrees / s², indicating a decelerating rotation state; if an axis exhibits a change from 30 degrees / s → -30 degrees / s → 30 degrees / s (period 5 seconds), it is identified as an oscillating rotation state. Simultaneously, the start time, duration (e.g., uniform rotation on the X-axis lasting 8 seconds), and transition process (e.g., the angular change of 25 degrees during the deceleration phase on the Z-axis) of each state are recorded. Finally, multi-axis motion state data containing the state type, characteristic parameters (angular velocity / angular acceleration), and time parameters for each axis are generated.

[0088] Furthermore, step S2 includes the following steps:

[0089] Step S21: Perform gyroscope multi-axis structure partitioning analysis based on gyroscope multi-axis motion state data to generate gyroscope multi-axis structure partitioning data;

[0090] Step S22: Analyze the gyroscope operation and temperature response characteristic parameters based on the gyroscope multi-axis structure partition data, and generate gyroscope operation-temperature response characteristic parameter data;

[0091] Step S23: Perform multi-axis temperature coupling response analysis of the gyroscope based on the gyroscope operation-temperature response characteristic parameter data, and generate multi-axis temperature response data of the gyroscope;

[0092] Step S24: Perform gyroscope multi-axis temperature disturbance torque analysis based on gyroscope multi-axis temperature response data to generate gyroscope multi-axis temperature disturbance torque data.

[0093] As an embodiment of the present invention, reference Figure 2 As shown, Figure 1 A detailed flowchart of step S2 is shown below. In this embodiment, step S2 includes the following steps:

[0094] Step S21: Perform gyroscope multi-axis structure partitioning analysis based on gyroscope multi-axis motion state data to generate gyroscope multi-axis structure partitioning data;

[0095] In this embodiment of the invention, based on the multi-axis motion state data of the gyroscope, the FPGA module (equivalent to an imported XC6SLX150, 176 PLIO channels) in the servo control circuit is used for structural partitioning analysis. The physical structure of the gyroscope is divided into three independent functional partitions: the sensing head partition (including a laser resonant cavity and photodetector, size 60mm×60mm), the drive circuit partition (including a DAC converter and power amplifier, occupying a PCB area of ​​40mm×30mm), and the detection circuit partition (including an ADC module and signal conditioning circuit, located in a 20mm×20mm area on the edge of the board). Through the coordinate mapping unit built into the FPGA, the X-axis motion state data (such as uniform rotation of 25 degrees / s) is bound to the sensing head partition, the Y-axis accelerated rotation data (angular acceleration of 10 degrees / s²) is associated with the drive circuit partition, and the Z-axis decelerated rotation data (angular acceleration of -50 degrees / s²) corresponds to the detection circuit partition. Simultaneously, the physical boundary parameters of each partition are recorded: the center coordinates of the sensitive head partition (board coordinates X=30mm, Y=30mm), the pin spacing of the drive circuit partition is 1.27mm, and the isolation distance between the detection circuit partition and other areas is 5mm. Finally, multi-axis structure partition data containing partition name, physical size, associated axis motion state, and spatial coordinates is generated and stored in 256kbit EEPROM.

[0096] Step S22: Analyze the gyroscope operation and temperature response characteristic parameters based on the gyroscope multi-axis structure partition data, and generate gyroscope operation-temperature response characteristic parameter data;

[0097] In this embodiment of the invention, based on the multi-axis structure partition data of the gyroscope, the operation and temperature response data of each partition are collected. In the sensing head partition, when the X-axis rotates at a constant speed of 25 degrees / s, the temperature value is recorded every 50ms. The initial temperature is 25 degrees Celsius, and it stabilizes at 32 degrees Celsius after 10 minutes of operation. The calculated temperature response time is 45s (the time to reach 63% of the stable value). In the driving circuit partition, under the acceleration state of 10 degrees / s² on the Y-axis, the initial temperature is 25 degrees Celsius, and it rises to 40 degrees Celsius after 3 minutes, with a temperature change rate of 0.083 degrees Celsius / s. Through the circuit's DSP module (800MHz main frequency), the characteristic parameters are analyzed: the temperature coefficient of the sensing head partition is 0.002 degrees / h / degree Celsius (the amount of drift increase per 1 degree Celsius increase), the heat capacity of the driving circuit partition is 50J / degree Celsius (the heat required to increase the temperature by 1 degree Celsius), and the thermal hysteresis time of the detection circuit partition under the deceleration state of the Z-axis is 20s (the time for temperature change to lag behind the change in motion state). Simultaneously, the resistance changes of each zone were measured over the full temperature range (-55°C to 85°C): the resistance of the sensitive head zone increased linearly with increasing temperature, with a temperature coefficient of 100 ppm / °C; the power transistor resistance of the drive circuit zone was 10 Ω at -55°C and 20 Ω at 85°C. Finally, operational-temperature response characteristic parameter data, including temperature response time, heat capacity, and resistance temperature coefficient, were generated and transmitted to the next unit via an RS-422 interface (full-duplex).

[0098] Step S23: Perform multi-axis temperature coupling response analysis of the gyroscope based on the gyroscope operation-temperature response characteristic parameter data, and generate multi-axis temperature response data of the gyroscope;

[0099] In this embodiment of the invention, based on the gyroscope's operating-temperature response characteristic parameter data, a PSOC processor with an integrated inertial computer is used to perform multi-axis temperature coupling response analysis. Using the processor's built-in heat conduction model, the coupling coefficient between the X-axis sensing head and the Y-axis drive circuit is calculated: when the sensing head temperature rises from 25 degrees Celsius to 32 degrees Celsius, the drive circuit temperature rises synchronously by 3 degrees Celsius, with a coupling coefficient of 0.43 (drive circuit temperature change / sensing head temperature change); the coupling coefficient between the Y-axis drive circuit and the Z-axis detection circuit is 0.2 (drive circuit temperature rises by 10 degrees Celsius, detection circuit temperature rises by 2 degrees Celsius). Using the finite element analysis module, the temperature field distribution during axis linkage is simulated: when the X-axis operates at 25 degrees / s + the Y-axis at 10 degrees / s², a temperature gradient is formed in the sensing head region with the center as the origin, and the edges are 2 degrees Celsius lower than the center; after adding the Z-axis deceleration motion of -50 degrees / s², the temperature gradient in the detection circuit region increases by 1 degree Celsius / mm. Temperature coupling strength was measured along each axis: the heat transfer rate between the XY axes was 5 W / m², the YZ axis was 3 W / m², and the XZ axis was 2 W / m². Combining this with gyroscope deflection center-of-gravity data (located 2 mm off-center from the center of the sensing head section), bearing friction characteristics were analyzed: for every 10°C increase in temperature, the friction torque of the X-axis bearing increased by 0.001 N·m, resulting in an additional 0.5°C increase in the sensing head temperature. Finally, multi-axis temperature response data including coupling coefficients, temperature gradients, and heat transfer rates were generated.

[0100] Step S24: Perform gyroscope multi-axis temperature disturbance torque analysis based on gyroscope multi-axis temperature response data to generate gyroscope multi-axis temperature disturbance torque data.

[0101] In this embodiment of the invention, temperature interference torque analysis is performed using the DSP module of the servo control circuit based on the multi-axis temperature response data of the gyroscope. First, the rotation-temperature change rate of each axis is calculated: when the X-axis rotates at 25 degrees / s, the temperature change rate of the sensing head is 0.002 degrees Celsius / s (72 degrees Celsius / h); when the Y-axis accelerates at 10 degrees / s², the temperature change rate of the drive circuit is 0.01 degrees Celsius / s (36 degrees Celsius / h). Temperature-induced phase shift is identified through the phase detection circuit: for every 1 degree Celsius change in X-axis temperature, the rotational phase shifts by 0.01 degrees, accumulating to 0.07 degrees at 32 degrees Celsius compared to 25 degrees Celsius; the phase shift caused by temperature change in the Y-axis is 1.5 times that of the X-axis. Interference torque is calculated based on the shift data: the X-axis uses the formula... (in The torque is determined by temperature disturbance, and k is a proportionality constant of 0.01 N·m·s / degree (characterizing the combined influence of phase shift and angular velocity on the torque). This is the temperature-induced phase shift. (where angular velocity is rotational velocity) when When the temperature is 0.07 degrees and ω = 25 degrees / s, the disturbance torque T = 0.01 × 0.07 × 25 = 0.0175 N·m; the Y-axis disturbance torque is 1.2 times that of the X-axis, i.e., 0.021 N·m; the Z-axis disturbance torque is 0.005 N·m due to the low rate of temperature change. Simultaneously, the frequency characteristics of the disturbance torque were measured: the X-axis disturbance torque has the same frequency as the rotation (0.4 Hz), while the Y-axis contains a second harmonic component (0.8 Hz). Finally, multi-axis temperature disturbance torque data containing the amplitude, phase, and frequency components of the disturbance torque for each axis are generated.

[0102] Furthermore, step S23 includes the following steps:

[0103] Step S231: Identify the multi-axis rotation nodes of the gyroscope based on the gyroscope operation-temperature response characteristic parameter data, and generate gyroscope multi-axis rotation node data;

[0104] Step S232: Perform gyroscope multi-axis linkage analysis based on gyroscope multi-axis rotation node data to generate gyroscope multi-axis linkage data;

[0105] Step S233: Analyze the temperature coupling strength of each axis of the gyroscope based on the multi-axis linkage data of the gyroscope, and generate temperature coupling strength data of each axis of the gyroscope.

[0106] Step S234: Perform multi-axis temperature coupling response analysis of the gyroscope based on the temperature coupling strength data of each axis of the gyroscope and the multi-axis linkage data of the gyroscope, and generate multi-axis temperature response data of the gyroscope.

[0107] As an embodiment of the present invention, reference Figure 3 As shown, Figure 2 A detailed flowchart of step S23 is shown in this embodiment. Step S23 includes the following steps:

[0108] Step S231: Identify the multi-axis rotation nodes of the gyroscope based on the gyroscope operation-temperature response characteristic parameter data, and generate gyroscope multi-axis rotation node data;

[0109] In this embodiment of the invention, based on the gyroscope's operating-temperature response characteristic parameter data, a current-frequency conversion circuit is used as the data processing carrier. This circuit has a three-channel analog current signal conversion function and supports the output of frequency signals after temperature compensation. By collecting the temperature response curves of each axis in the circuit, the focus is on capturing the time points of abrupt changes in the rate of temperature change: When the temperature of the X-axis sensing head rises from 25 degrees Celsius to 28 degrees Celsius, the rate of change jumps from 0.001 degrees Celsius / s to 0.003 degrees Celsius / s, corresponding to a rotation node; the Y-axis drive circuit shows a temperature plateau at 30 degrees Celsius (without change for 2 seconds), which is determined to be a rotation node; during the temperature drop process, the rate of change of the Z-axis detection circuit changes from -0.002 degrees Celsius / s to -0.001 degrees Celsius / s at -50 degrees Celsius, which is marked as a rotation node. Simultaneously, the physical positions of each node are recorded: the X-axis node is located at the connection between the sensing head and the drive axis (3mm from the center), the Y-axis node is at the power transistor pin solder joint (coordinates X=20mm, Y=15mm), and the Z-axis node is at the edge of the ADC chip heatsink (5mm from the circuit center). Finally, multi-axis rotation node data containing node temperature values, timestamps, physical coordinates, and temperature change rate abrupt changes are generated.

[0110] Step S232: Perform gyroscope multi-axis linkage analysis based on gyroscope multi-axis rotation node data to generate gyroscope multi-axis linkage data;

[0111] In this embodiment of the invention, multi-axis linkage analysis is performed using a fiber optic gyroscope control circuit based on the multi-axis rotation node data of the gyroscope. The circuit receives node trigger signals for the X, Y, and Z axes (via an RS-422 interface, baud rate 10Mbps). When the X-axis node (28 degrees Celsius) is triggered, the real-time temperatures of the Y and Z axes are recorded simultaneously: the Y-axis temperature is 27 degrees Celsius (below its own node temperature of 30 degrees Celsius), and the Z-axis temperature is -48 degrees Celsius (below its own node temperature of -50 degrees Celsius), indicating that the Y and Z axes are in a following state when the X-axis is triggered. When the Y-axis node (30 degrees Celsius) is triggered, the X-axis temperature has risen to 32 degrees Celsius (exceeding its own node temperature), and the Z-axis temperature is -49 degrees Celsius, indicating that the X-axis is in a linked state when the Y-axis is triggered. The time difference between the triggers of each axis node is calculated: the trigger interval between the X-axis node and the Y-axis node is 15 seconds, and the trigger interval between the Y-axis node and the Z-axis node is 20 seconds. Simultaneously, the angular velocity correlation under the linkage state was measured: when the X-axis rotates at 25 degrees / s, the correlation coefficient of the Y-axis angular velocity with the X-axis is 0.6 (Y-axis angular velocity = 0.6 × X-axis angular velocity), and the Z-axis correlation coefficient is 0.3. Finally, multi-axis linkage data containing inter-axis trigger timing, correlation coefficients, and linkage state (following / independent) is generated.

[0112] Step S233: Analyze the temperature coupling strength of each axis of the gyroscope based on the multi-axis linkage data of the gyroscope, and generate temperature coupling strength data of each axis of the gyroscope.

[0113] In this embodiment of the invention, the temperature coupling strength of each axis is analyzed based on the multi-axis linkage data of the gyroscope. The temperature values ​​of the X, Y, and Z axes in the linkage state are synchronously collected using thermocouple sensors (accuracy ±0.1 degrees Celsius): During the process of the X-axis rising from 28 degrees Celsius to 35 degrees Celsius, the Y-axis temperature rises from 27 degrees Celsius to 31 degrees Celsius, and the calculated temperature coupling coefficient of X to Y is 0.57 (Y-axis temperature change / X-axis temperature change = 4 degrees Celsius / 7 degrees Celsius); when the Y-axis rises from 30 degrees Celsius to 40 degrees Celsius, the Z-axis temperature rises from -49 degrees Celsius to -45 degrees Celsius, and the coupling coefficient of Y to Z is 0.4 (Z-axis temperature change / Y-axis temperature change = 4 degrees Celsius / 10 degrees Celsius); the direct coupling coefficient between the X and Z axes is 0.2 (when the X-axis temperature rises by 5 degrees Celsius, the Z-axis temperature rises by 1 degree Celsius). Simultaneously, the coupling changes under different linkage intensities were measured: when the X and Y axes rotated simultaneously (angular velocities of 25° / s and 20° / s respectively), the coupling coefficient increased to 0.7; when rotating a single axis, the coupling coefficient decreased to 0.3. This was calculated using the thermal resistance formula (…). , For interaxial thermal resistance, The temperature difference between the two axes. (Based on the power consumption of the heat-generating components), the thermal resistance between the XY axes is found to be 5°C / W, the YZ axis is 8°C / W, and the XZ axis is 10°C / W. Lower thermal resistance indicates higher coupling strength. Finally, temperature coupling strength data containing the relationships between inter-axis coupling coefficients, thermal resistance, and linkage strength is generated.

[0114] Step S234: Perform multi-axis temperature coupling response analysis of the gyroscope based on the temperature coupling strength data of each axis of the gyroscope and the multi-axis linkage data of the gyroscope, and generate multi-axis temperature response data of the gyroscope.

[0115] In this embodiment of the invention, multi-axis temperature coupling response analysis is performed based on the temperature coupling strength data of each axis of the gyroscope and the multi-axis linkage data of the gyroscope. The three-dimensional thermal field simulation module is invoked, and the XY-axis coupling coefficient of 0.57 and thermal resistance of 5 degrees Celsius / W are input into the model. The simulation shows that when the X-axis rotates at 25 degrees / s for 10 minutes (temperature rises to 35 degrees Celsius), the Y-axis temperature rises to 31 degrees Celsius due to coupling, which is 2 degrees Celsius higher than when running independently. Combined with the YZ-axis coupling coefficient of 0.4, the Z-axis temperature is calculated to rise by 1.6 degrees Celsius due to the influence of the Y-axis. Verification using an infrared thermometer (accuracy ±0.5 degrees Celsius) shows that when the X-axis temperature is 35 degrees Celsius, the actual Y-axis temperature is 30.8 degrees Celsius, with an error ≤0.2 degrees Celsius; when the Y-axis temperature is 31 degrees Celsius, the actual Z-axis temperature is -45.2 degrees Celsius, with an error ≤0.2 degrees Celsius. Simultaneously, the time delay of the coupled response was analyzed: after the X-axis temperature change, the Y-axis temperature response delayed by 2 seconds, and the Z-axis response delayed by 3 seconds (indirectly affected by the Y-axis). Based on the correlation coefficient in the linkage data, the coupling model was corrected: when the X and Y axis linkage strength increased to 0.7, the coupling response amplitude increased by 15%, and the delay time was shortened to 1.5 seconds. Finally, multi-axis temperature response data containing the coupled temperature values ​​of each axis, response delay, and simulation and measurement errors were generated.

[0116] Furthermore, step S234 includes the following steps:

[0117] Based on the multi-axis linkage data of the gyroscope, the gyroscope deflection center of gravity is identified, and gyroscope deflection center of gravity data is generated.

[0118] In this embodiment of the invention, based on multi-axis linkage data from a gyroscope, a current-frequency conversion circuit is used as the data processing carrier. This circuit has a built-in crystal oscillator and temperature sensor, which can simultaneously acquire three-axis motion and temperature signals. The circuit receives angular velocity signals of the X, Y, and Z axes (X-axis 25° / s, Y-axis 20° / s, Z-axis 15° / s) through its 16 analog channels. Combined with the correlation coefficients in the multi-axis linkage data (XY-axis 0.6, YZ-axis 0.4), the distribution of rotational inertia for each axis is calculated: the X-axis rotational inertia is concentrated at a distance of 3mm from the center, the Y-axis at 5mm, and the Z-axis at 4mm. The center of gravity is then calculated using the formula (…). ,in For the coordinates of the center of gravity, Let the moment of inertia of each axis be , The distances from the inertia concentration points of each axis to the mechanical center are used to determine the coordinates of the deflection center of gravity (X=2.8mm, Y=4.2mm, Z=3.5mm), which are offset by 0.8mm relative to the mechanical center of the gyroscope. Simultaneously, through actual measurement and verification using a laser displacement sensor (accuracy ±0.01mm), the deviation between the center of gravity offset and the calculated value is ≤0.02mm in three-axis linkage mode. Finally, deflection center of gravity data is generated, including the three-dimensional coordinates of the center of gravity, the offset, and the contribution ratio of inertia to each axis.

[0119] Preferably, the friction characteristics of the multi-bearing gyroscope are analyzed based on the temperature coupling strength data of each axis of the gyroscope and the deflection center of gravity data of the gyroscope, and the friction characteristic data of the multi-bearing gyroscope are generated.

[0120] In this embodiment of the invention, multi-bearing friction characteristics are analyzed based on the temperature coupling strength data of each axis of the gyroscope and the gyroscope deflection center of gravity data. The circuit records the change in the coefficient of friction of the X-axis bearing (located on the X-axis extension line of the center of gravity) in the temperature range of 25°C to 40°C through a temperature acquisition module (accuracy <0.3°C): the coefficient of friction is 0.002 at 25°C, 0.0025 at 30°C, and 0.0035 at 40°C, and the calculated temperature coefficient is 0.0001 / °C. For the Y-axis bearing (close to the center of gravity at the Y coordinate), under the coupling effect, the temperature rises from 27°C to 31°C, and the coefficient of friction increases from 0.0018 to 0.0022, with an additional increase of 0.0001 due to the X-axis coupling. The Z-axis bearing, due to its higher thermal resistance, has a smaller temperature change range, and the coefficient of friction remains stable at 0.0015±0.0001. Simultaneously, friction was measured under different center of gravity offsets: with an offset of 0.8 mm, the X-axis bearing friction was 0.0015 N·m, an increase of 0.0003 N·m compared to no offset. Finally, friction characteristic data including the friction coefficient, temperature coefficient, and relationship between friction and center of gravity offset for each bearing were generated.

[0121] Preferably, gyroscope multi-axis temperature coupling response analysis is performed based on gyroscope temperature coupling strength data of each axis, gyroscope multi-bearing friction characteristic data, and gyroscope deflection center of gravity data to generate gyroscope multi-axis temperature response data.

[0122] In this embodiment of the invention, based on the temperature coupling strength data of each axis of the gyroscope, the frictional characteristics data of the multi-bearing system of the gyroscope, and the deflection center of gravity data of the gyroscope, an integrated inertial computer system is used to perform multi-axis temperature coupling response analysis. A multi-axis temperature coupling response model of the gyroscope is constructed, with the XY axis thermal resistance of 5 degrees Celsius / W as input. It is calculated that when the X-axis temperature rises by 10 degrees Celsius due to frictional heat generation (friction 0.0015 N·m, power 0.5 W), the Y-axis absorbs 0.57 × 0.5 W = 0.285 W of heat through coupling, resulting in a temperature rise of 5.7 degrees Celsius. Considering the additional frictional contribution of a 0.8 mm center of gravity offset to the Y-axis bearing (increasing friction by 0.0003 N·m), the Y-axis temperature rise is corrected to 6.2 degrees Celsius. Measured data using thermocouple arrays (distributed at each bearing location) showed that after a 10°C increase in X-axis temperature, the actual Y-axis temperature increase was 6.1°C, with an error ≤0.1°C. The Z-axis, affected by Y-axis coupling, increased in temperature by 2.5°C, with a deviation ≤0.02°C from the model's calculated value (0.4 × 6.2°C = 2.48°C). Response time analysis revealed that after the X-axis temperature change, the Y-axis temperature reached a stable value in 8 seconds, and the Z-axis in 12 seconds, both consistent with the model's predicted heat conduction delays (7 seconds for Y-axis and 11 seconds for Z-axis). Finally, multi-axis temperature response data was generated, including the coupled temperature increases for each axis, response times, and the deviations between the model and measured values.

[0123] Furthermore, step S24 includes the following steps:

[0124] Step S241: Analyze the multi-axis rotation and temperature change rate of the gyroscope based on the multi-axis temperature response data of the gyroscope, and generate multi-axis rotation-temperature change rate data of the gyroscope.

[0125] In this embodiment of the invention, the multi-axis rotation and temperature change rate of the gyroscope are analyzed based on the multi-axis temperature response data of the gyroscope. Real-time rotation data of the X, Y and Z axes are collected through three analog current input channels. The rotational angular velocity of the X axis is stabilized at 25 degrees / s, the Y axis increases from 10 degrees / s to 30 degrees / s with an acceleration of 10 degrees / s², and the Z axis maintains a uniform rotation speed of 15 degrees / s. Temperature sensor data acquisition was initiated synchronously, with a sampling interval set to 100ms, and continuous recording for 1 hour: The X-axis temperature rose from an initial 25°C to 32°C, a total change of 7°C, with a calculated temperature change rate of 0.00194°C / s (7°C ÷ 3600s); the Y-axis temperature rose from 25°C to 40°C, a change of 15°C, with a temperature change rate of 0.00417°C / s; and the Z-axis temperature rose from 25°C to 28°C, a change of 3°C, with a temperature change rate of 0.00083°C / s. Simultaneously, the rotational angular velocity was converted into a frequency signal (256kHz) through the circuit's frequency output port (TTL level, 5V logic), and time-correlated with the temperature change rate, confirming that for every 1°C / s increase in X-axis angular velocity, the temperature change rate increases by 0.00008°C / s. The final result is a multi-axis rotation-temperature change rate data set containing the rotational angular velocity of each axis, the total temperature change, the real-time temperature change rate, and the correlation coefficient between the two.

[0126] Step S242: Based on the gyroscope multi-axis rotation-temperature change rate data, identify the gyroscope temperature-induced multi-axis rotation phase shift and generate gyroscope temperature-induced multi-axis rotation phase shift data;

[0127] In this embodiment of the invention, the temperature-induced multi-axis rotation phase shift of the gyroscope is identified based on the gyroscope's multi-axis rotation-temperature change rate data. The circuit receives a 25°C / s rotation signal on the X-axis and a 0.00194°C / s temperature change rate signal. A built-in phase comparator (accuracy 0.001°C) compares the rotation reference phase (generated from a 1024kHz reference frequency) with the actual rotation phase: at 25°C, the phase difference is 0°C; when the temperature rises to 32°C, the actual phase lags the reference phase by 0.07°C, calculating a phase shift of 0.01°C per degree Celsius. For the Y-axis, under a rotation speed of 30 degrees / s and a temperature change of 0.00417 degrees Celsius / s, the same method was used to measure the following: as the temperature increased from 25 degrees Celsius to 40 degrees Celsius, the phase led by 0.15 degrees, with a phase shift of 0.01 degrees per degree Celsius. Due to the influence of acceleration, the phase shift increased linearly with temperature (the shift increased by 0.05 degrees for every 5 degrees Celsius increase in temperature). For the Z-axis, under a rotation speed of 15 degrees / s, the temperature change caused a phase shift of 0.03 degrees, with a shift of 0.01 degrees per degree Celsius, and no additional shift caused by acceleration. The phase shift data and temperature changes were synchronously recorded through the differential signal output port of the circuit, forming phase shift curves for each axis across the entire temperature range (-55 degrees Celsius to 85 degrees Celsius). Finally, temperature-induced multi-axis rotational phase shift data was generated, including the phase shift amount for each axis, the shift coefficient per degree Celsius, and the shift direction (leading / lagging).

[0128] Step S243: Perform gyroscope multi-axis temperature disturbance torque analysis based on gyroscope temperature-induced multi-axis rotation phase shift data to generate gyroscope multi-axis temperature disturbance torque data.

[0129] In this embodiment of the invention, the multi-axis temperature disturbance torque of the gyroscope is analyzed based on the temperature-induced multi-axis rotational phase shift data of the gyroscope. According to the formula... (in The disturbance torque is given by k, which is a proportionality constant of 0.01 N·m·s / degree. This is the phase offset. The following calculations were performed using rotational angular velocity: For the X-axis, with a phase shift of 0.07 degrees and a rotation of 25 degrees / s, the interference torque is 0.07 × 25 × 0.001 = 0.00175 N·m; for the Y-axis, with a phase shift of 0.15 degrees and a rotation of 30 degrees / s, the interference torque is 0.15 × 30 × 0.001 = 0.0045 N·m; and for the Z-axis, with a phase shift of 0.03 degrees and a rotation of 15 degrees / s, the interference torque is 0.03 × 15 × 0.001 = 0.00045 N·m. Simultaneously, considering the circuit's channel isolation parameter (10 ppm), the effects of inter-axis crosstalk were corrected: X-axis crosstalk to the Y-axis increased the Y-axis interference torque by 0.0001 N·m, and Y-axis crosstalk to the Z-axis increased by 0.00005 N·m. The final corrected interference torque is 0.0046 N·m for the Y-axis and 0.0005 N·m for the Z-axis. The calculation results are bound to real-time acquired temperature and angular velocity data through the circuit's bus output port to form a complete characteristic curve of the interference torque changing with temperature (e.g., the interference torque increases by 0.00025 N·m for every 1 degree Celsius increase in X-axis temperature). Finally, multi-axis temperature interference torque data, including the absolute value of the interference torque on each axis, crosstalk correction value, and temperature-dependent characteristics, is generated and transmitted to the subsequent processing unit via an isolated RS422 interface.

[0130] Furthermore, step S3 includes the following steps:

[0131] Step S31: Collect gyroscope environmental data through sensors, extract gyroscope environmental parameter features based on the gyroscope environmental data, and generate gyroscope environmental parameter feature data;

[0132] In this embodiment of the invention, environmental data from a gyroscope is collected using sensors. A temperature sensor collects ambient temperature data ranging from -55°C to 85°C at 100ms intervals, with an accuracy of ±0.3°C, continuously recording for one hour to obtain a temperature sequence. A humidity sensor simultaneously collects relative humidity data from 20% to 80%RH, with an error of ±2%, generating a humidity change curve. An electromagnetic interference detector measures interference intensity in the 10kHz-1GHz frequency band, with a range of 0-100dBμV, recording a peak value (e.g., ≤50dBμV) every 500ms. The collected data is transmitted to the FPGA module of the circuit and processed using a feature extraction algorithm: temperature features include average temperature (28°C), temperature change rate (0.002°C / s), and extreme value difference (7°C); humidity features include humidity fluctuation amplitude (±5%) and correlation coefficient with temperature (0.3); electromagnetic interference features include dominant frequency component (50Hz) and peak duration (10ms). Finally, environmental parameter feature data containing feature parameters of temperature, humidity, electromagnetic interference, and timestamps is generated.

[0133] Step S32: Perform gyroscope multi-axis thermal drift mapping processing based on gyroscope environmental parameter characteristic data and gyroscope multi-axis temperature disturbance torque data to generate gyroscope multi-axis thermal drift mapping data;

[0134] In this embodiment of the invention, gyroscope multi-axis thermal drift mapping processing is performed based on gyroscope environmental parameter characteristic data and gyroscope multi-axis temperature disturbance torque data. The circuit receives the X-axis ambient temperature change rate of 0.002 degrees Celsius / s and disturbance torque of 0.00175 N·m, and calculates the result using the built-in mapping model and the formula. (in For the corresponding Thermal drift of the shaft For the corresponding Shaft temperature coefficient, For temperature change, The thermal drift of each axis was calculated separately (using environmental interference constants): For every 1 degree Celsius increase in temperature, the X-axis thermal drift increased by 0.02 degrees / h (based on a temperature coefficient of 15 ppm / degree Celsius); considering humidity fluctuations of ±5%, the drift value was corrected to 0.021 degrees / h (for every 10% change in humidity, the drift increased by 5%); the 50Hz component of electromagnetic interference caused an additional 0.001 degrees / h of drift, and the final X-axis mapping relationship was "thermal drift = 0.021 × ΔT + 0.001". For the Y-axis, with an ambient temperature change rate of 0.004 degrees Celsius / s and an interference torque of 0.0046 N·m, the calculated thermal drift was 0.03 × ΔT + 0.002 degrees / h; the Z-axis thermal drift was 0.015 × ΔT + 0.0005 degrees / h. The mapping relationship was converted into a pulse signal through the frequency output port (256 kHz) of the circuit, with each 1 degree / h drift corresponding to 100 pulses / s. Finally, multi-axis thermal drift mapping data is generated, which includes quantitative mapping formulas and correction coefficients for thermal drift and environmental parameters of each axis.

[0135] Step S33: Perform gyroscope temperature drift detection based on gyroscope multi-axis thermal drift mapping data to generate gyroscope temperature drift data.

[0136] In this embodiment of the invention, gyroscope temperature drift is detected based on multi-axis thermal drift mapping data. The system calls the mapping formula to calculate the real-time environmental data: when the X-axis ambient temperature rises from 25 degrees Celsius to 32 degrees Celsius (ΔT=7 degrees Celsius), substituting into the formula, we get thermal drift = 0.021×7+0.001=0.148 degrees / h; by measuring the X-axis output pulse through a quantizer (scale coefficient 20365Hz / mA), the actual drift is calculated to be 0.147 degrees / h, with an error ≤0.001 degrees / h. The ambient temperature on the Y-axis rises to 40 degrees Celsius (ΔT=15 degrees Celsius), with a calculated drift of 0.03×15+0.002=0.452 degrees / h and an actual measured drift of 0.451 degrees / h. On the Z-axis, ΔT=3 degrees Celsius, the calculated drift is 0.015×3+0.0005=0.0455 degrees / h, and the actual measured drift is 0.045 degrees / h. During the detection process, the system updates the drift value every 10 seconds, synchronously recording environmental parameters and drift data via the 1553B bus to form a full-temperature-range (-55 degrees Celsius to 85 degrees Celsius) drift curve: the X-axis drifts +0.02 degrees / h at -55 degrees Celsius, -0.005 degrees / h at 25 degrees Celsius, and +0.03 degrees / h at 85 degrees Celsius; the Y and Z axes show consistent trends, with an amplitude difference ≤0.005 degrees / h. The final output includes temperature drift data containing full-temperature-range drift values ​​for each axis, measured errors, and detection timestamps.

[0137] Furthermore, step S32 includes the following steps:

[0138] Step S321: Analyze the fluctuation relationship of the gyroscope's environmental factors based on the gyroscope's environmental parameter characteristic data, and generate environmental factor fluctuation relationship data;

[0139] In this embodiment of the invention, the environmental factor fluctuation relationship of the gyroscope is analyzed based on the characteristic data of the gyroscope's environmental parameters. The circuit uses a 16-bit ADC module (sampling rate 250Ksps) to perform time-series correlation on the characteristic data of temperature (25°C~32°C), humidity (40%~60%RH), and electromagnetic interference (30~50dBμV), calculating the covariance between factors every 100ms: the covariance between temperature and humidity is 0.8 (positively correlated), the covariance between temperature and electromagnetic interference is 0.3 (weakly correlated), and the covariance between humidity and electromagnetic interference is 0.1 (almost unrelated). A sliding window algorithm (window length 10s) is used to extract the fluctuation period: temperature fluctuation period is 200s, humidity fluctuation period is 300s, and electromagnetic interference fluctuation period is 50s. Simultaneously, the fluctuation amplitude percentage of each factor is calculated: temperature fluctuation contributes 60% of the total environmental change, humidity accounts for 30%, and electromagnetic interference accounts for 10%. Finally, environmental factor fluctuation relationship data containing environmental factor covariance matrix, fluctuation period, and contribution ratio are generated and stored in the circuit's 32MB NORFLASH.

[0140] Step S322: Analyze the temperature, humidity and electromagnetic noise disturbance intensity of the gyroscope based on the environmental factor fluctuation relationship data, and generate temperature, humidity and electromagnetic noise disturbance intensity data.

[0141] In this embodiment of the invention, the temperature, humidity, and electromagnetic noise disturbance intensity of the gyroscope are analyzed based on environmental factor fluctuation data. The circuit converts a temperature fluctuation period of 200s and an amplitude of 7 degrees Celsius into temperature disturbance intensity using the formula... (in For disturbance intensity, For amplitude, (Period is 0.035 degrees Celsius / s); humidity fluctuation amplitude 20%RH, period 300s, disturbance intensity 0.067%RH / s; electromagnetic interference amplitude 20dBμV, period 50s, disturbance intensity 0.4dBμV / s. Combining factor contribution ratio corrections: temperature disturbance intensity weighted is 0.035×60%=0.021 degrees Celsius / s, humidity is 0.067×30%=0.020%RH / s, electromagnetic interference is 0.4×10%=0.04dBμV / s. The disturbance intensity is converted into a frequency signal through the circuit's TTL output port (5V logic). Temperature 0.01 degrees Celsius / s corresponds to 100Hz, humidity 0.01%RH / s corresponds to 50Hz, and electromagnetic interference 0.1dBμV / s corresponds to 200Hz. Finally, temperature and humidity-electromagnetic noise disturbance intensity data containing the weighted disturbance intensity of each factor and frequency conversion coefficient are generated and transmitted to the power board grounding terminal through a separate wire to reduce interference.

[0142] Step S323: Based on the temperature and humidity-electromagnetic noise disturbance intensity data, perform gyroscope axis offset and temperature trend analysis on the gyroscope multi-axis temperature disturbance torque data to generate gyroscope axis offset-temperature trend data;

[0143] In this embodiment of the invention, the offset and temperature trend of each axis of the gyroscope are analyzed based on the temperature, humidity, and electromagnetic noise disturbance intensity data. The circuit receives an X-axis disturbance torque of 0.00175 N·m and a temperature disturbance intensity of 0.021 degrees Celsius / s. Using the built-in linear regression module, it calculates that for every 0.01 degrees Celsius / s increase in temperature, the X-axis offset increases by 0.005 degrees / h (correlation coefficient 0.9); humidity disturbance causes an additional 0.001 degrees / h offset, and electromagnetic interference causes an additional 0.0005 degrees / h. For the Y-axis, with an disturbance torque of 0.0046 N·m and a temperature disturbance of 0.021 degrees Celsius / s, it is determined that for every 0.01 degrees Celsius / s of temperature, there is an offset of 0.01 degrees / h, and humidity adds 0.002 degrees / h. For the Z-axis, with an disturbance torque of 0.00045 N·m, for every 0.01 degrees Celsius / s of temperature, there is an offset of 0.003 degrees / h. Simultaneously, trend curves of each axis offset as a function of temperature were plotted: the X-axis showed linear growth (slope 0.005 degrees / (h·°C / s)), the Y-axis showed quadratic growth (slope increases with increasing temperature), and the Z-axis showed linear growth (slope 0.003 degrees / (h·°C / s)). Finally, trend data of each axis offset-temperature perturbation function and additional offsets were generated and stored in a 64MB EMMC.

[0144] Step S324: Analyze the multi-axis thermal stress distribution characteristics of the gyroscope based on the offset-temperature trend data of each axis, and generate multi-axis thermal stress distribution characteristic data of the gyroscope.

[0145] In this embodiment of the invention, the multi-axis thermal stress distribution characteristics of the gyroscope are analyzed based on the offset-temperature trend data of each axis. The thermal stress model is called, and the input temperature gradient of 0.5 degrees Celsius / mm corresponds to an X-axis offset of 0.005 degrees / h. The calculated thermal stress in the sensing head area is 50 MPa (stress = elastic modulus × temperature gradient × coefficient of linear expansion, elastic modulus 200 GPa, coefficient of linear expansion 12 ppm / degree Celsius); the thermal stress in the drive circuit area is 80 MPa, corresponding to a Y-axis offset of 0.01 degrees / h and a temperature gradient of 0.8 degrees Celsius / mm; and the thermal stress in the detection circuit area is 30 MPa, corresponding to a Z-axis offset of 0.003 degrees / h and a temperature gradient of 0.3 degrees Celsius / mm. The stress distribution data is transmitted through the circuit's SPI interface (10 Mbps), and a thermal stress cloud map is plotted: the X-axis stress is concentrated at the bearing connection (60 MPa), the Y-axis stress is concentrated at the power transistor solder joint (90 MPa), and the Z-axis stress is uniformly distributed (30 ± 5 MPa). Simultaneously, stress relaxation times were calculated: 200s for the X-axis, 150s for the Y-axis, and 300s for the Z-axis. This ultimately generated multi-axis thermal stress distribution characteristic data, including thermal stress values, distribution areas, and relaxation times for each axis.

[0146] Step S325: Perform gyroscope multi-axis thermal drift mapping processing based on the gyroscope's axis offset-temperature trend data and gyroscope multi-axis thermal stress distribution characteristic data to generate gyroscope multi-axis thermal drift mapping data.

[0147] In this embodiment of the invention, gyroscope multi-axis thermal drift mapping is performed based on the offset-temperature trend data of each axis of the gyroscope and the multi-axis thermal stress distribution characteristic data of the gyroscope. A three-dimensional thermal drift mapping model is constructed, and the thermal drift mapping formula is as follows: ,in Thermal drift value, For axis offset, For temperature change, This is the thermal stress correction factor. The basic drift constant is used. Substituting an X-axis offset of 0.005 degrees / h and a thermal stress of 50 MPa into the model, a thermal drift correction factor of 1.2 is calculated (the drift is amplified by 10% for every 10 MPa increase in stress). The corrected X-axis mapping relationship is "thermal drift = 0.005 × ΔT × 1.2 + 0.0015" (ΔT is the temperature change). For a Y-axis offset of 0.01 degrees / h and a thermal stress of 80 MPa, with a correction factor of 1.5, the mapping relationship is "thermal drift = 0.01 × ΔT × 1.5 + 0.003"; for a Z-axis offset of 0.003 degrees / h and a thermal stress of 30 MPa, with a correction factor of 1.1, the mapping relationship is "thermal drift = 0.003 × ΔT × 1.1 + 0.0005". The mapping data is output through the data interface, and the model parameters are updated every 10 seconds. Combined with actual measurement verification, the X-axis drift is calculated to be 0.042 degrees / h at ΔT=7 degrees Celsius, and the measured drift is 0.041 degrees / h, with an error ≤0.001 degrees / h. Finally, a multi-axis thermal drift mapping data generator is generated, which includes the corrected mapping formulas for each axis and the verification error.

[0148] Furthermore, step S4 includes the following steps:

[0149] Step S41: Perform multi-axis rotation and temperature change analysis of the gyroscope based on the gyroscope temperature drift data to generate multi-axis rotation-temperature change data of the gyroscope;

[0150] In this embodiment of the invention, gyroscope multi-axis rotation and temperature change analysis are performed based on gyroscope temperature drift data. The circuit acquires X, Y, and Z axis data through three synchronous acquisition channels: X-axis temperature drift data is +0.02 degrees / h at -55 degrees Celsius, -0.005 degrees / h at 25 degrees Celsius, and +0.03 degrees / h at 85 degrees Celsius, corresponding to a rotational angular velocity of 25 degrees / s; Y-axis drift data is +0.025 degrees / h at -55 degrees Celsius, -0.004 degrees / h at 25 degrees Celsius, and +0.035 degrees / h at 85 degrees Celsius, corresponding to a rotational angular velocity of 30 degrees / s; Z-axis drift data is +0.015 degrees / h at -55 degrees Celsius, -0.006 degrees / h at 25 degrees Celsius, and +0.025 degrees / h at 85 degrees Celsius, corresponding to a rotational angular velocity of 15 degrees / s. The circuit's built-in temperature coefficient calculation module performs linear fitting on the data for each axis: for every 1 degree Celsius temperature change on the X-axis, the drift change is 0.0005 degrees / h ((0.03-0.02) / (85+55)); for the Y-axis, it's 0.00058 degrees / h / degree Celsius; and for the Z-axis, it's 0.00042 degrees / h / degree Celsius. Simultaneously, the temperature response delay under rotational conditions is recorded: after a temperature change on the X-axis, the drift value stabilizes in 10 seconds; for the Y-axis, 8 seconds; and for the Z-axis, 12 seconds. Finally, multi-axis rotation-temperature change data is generated, including rotational angular velocity, full-temperature-range drift value, temperature coefficient, and response delay for each axis.

[0151] Step S42: Based on the multi-axis rotation-temperature change data of the gyroscope, identify the multi-axis temperature-induced dynamic imbalance characteristics of the gyroscope based on the multi-axis motion state data of the gyroscope, and generate multi-axis temperature-induced dynamic imbalance characteristic data of the gyroscope.

[0152] In this embodiment of the invention, the multi-axis motion state data of the gyroscope is analyzed based on the multi-axis rotation-temperature change data of the gyroscope to identify the temperature-induced dynamic imbalance characteristics of the gyroscope. The circuit receives the motion state data of the X-axis rotating at a constant speed (25 degrees / s), and combines it with its temperature coefficient of 0.0005 degrees / h / degree Celsius. Through imbalance detection algorithm analysis, when the temperature rises from 25 degrees Celsius to 85 degrees Celsius (a change of 60 degrees Celsius), the theoretical cumulative drift value is calculated to be 0.0005 × 60 = 0.03 degrees / h. Compared with the actual measured value of 0.035 degrees / h, the difference of 0.005 degrees / h is the temperature-induced dynamic imbalance, which is manifested as a 0.01mm offset of the rotation axis center. For the Y-axis, with accelerated rotation (10°C / s²) and a temperature change of 60°C, the theoretical drift is 0.00058 × 60 = 0.035°C / h, the measured drift is 0.042°C / h, the weight imbalance is 0.007°C / h, corresponding to a center offset of 0.014mm. Due to the acceleration, the weight imbalance increases linearly with temperature (0.002mm for every 10°C increase in temperature). The weight imbalance for the Z-axis with uniform rotation (15°C / s) is 0.003°C / h, corresponding to a center offset of 0.006mm. The circuit outputs the imbalance characteristics of each axis via an RS422 interface: the X-axis imbalance mainly manifests as periodic fluctuations (the period is consistent with the rotation period); the Y-axis shows continuous offset; and the Z-axis shows random small fluctuations. Finally, multi-axis temperature-induced dynamic imbalance characteristic data is generated, including the weight imbalance of each axis, center offset value, and imbalance mode.

[0153] Step S43: Based on the multi-axis temperature-induced dynamic imbalance characteristic data of the gyroscope, perform gyroscope temperature drift influence area location analysis and generate gyroscope temperature drift influence area location data;

[0154] In this embodiment of the invention, the influence area of ​​gyroscope temperature drift is located and analyzed based on the multi-axis temperature-induced dynamic imbalance characteristic data of the gyroscope. The circuit associates the 0.01mm center offset corresponding to a 0.005°C / h X-axis imbalance with the gyroscope structural partition data: through coordinate mapping (with the mechanical center of the gyroscope as the origin), the offset direction is determined to point to the sensitive head partition (X=30mm, Y=30mm), and the affected area is a 2mm range from the edge of the sensitive head. The temperature coefficient of this area is 15ppm / °C, which is consistent with the imbalance calculation results. The 0.014mm Y-axis offset points to the drive circuit partition (X=20mm, Y=15mm), and the affected area is a 1.5mm range around the power transistor. The thermal resistance of this area is 5°C / W, and heat accumulation exacerbates the imbalance. The 0.006mm Z-axis offset points to the detection circuit partition (X=50mm, Y=50mm), and the affected area is at the ADC chip pin. The channel isolation of this area is 25ppm, and crosstalk causes a minor imbalance. The circuit was verified through actual testing using a laser ranging module (accuracy ±0.001mm): the temperature at the edge of the X-axis sensing head was 2 degrees Celsius higher than the center, consistent with the positioning area; the temperature gradient of the Y-axis drive circuit partition was 0.5 degrees Celsius / mm, consistent with the thermal accumulation characteristics. Finally, drift-affected area positioning data was generated, including the coordinates, physical boundaries, and temperature characteristics of the affected areas for each axis.

[0155] Step S44: Perform gyroscope temperature compensation processing based on the gyroscope temperature drift affected area positioning data and gyroscope multi-axis rotation-temperature change data to generate temperature-compensated gyroscope multi-axis motion state data.

[0156] In this embodiment of the invention, gyroscope temperature compensation processing is performed based on gyroscope temperature drift influence area location data and gyroscope multi-axis rotation-temperature change data. ,in For temperature compensation amount, For real-time temperature, For the temperature coefficients of each shaft, This refers to the runtime. The system's temperature coefficient is calculated for the X-axis sensitive head region (affected area X=30±2mm, Y=30±2mm). When the X-axis runs at 85 degrees Celsius for 1 hour, the compensation amount The angular velocity in the original motion data is corrected using the positioning data of the gyroscope temperature drift-affected area (subtracting the angular velocity component of 0.000012 degrees / s corresponding to 0.0425 degrees / h); for the Y-axis drive circuit area, the temperature coefficient... Compensation amount when running at 85 degrees Celsius for 1 hour Corrected angular velocity component: 0.0000137 degrees / s; Z-axis temperature coefficient Compensation amount when running at 85 degrees Celsius for 1 hour The correction is 0.0000099 degrees / s. During the compensation process, the system updates the real-time temperature (acquired by the built-in 18B20 sensor, with an accuracy of <0.3 degrees Celsius) every 100ms, and outputs the compensation voltage (0-5V) to the gyroscope servo circuit through the 16-bit DAC module to achieve mechanical correction. The final temperature-compensated data has an angular velocity error of ≤0.000001 degrees / s for each axis.

[0157] Furthermore, step S5 includes the following steps:

[0158] Step S51: Perform gyroscope multi-axis attitude drift deviation analysis based on the temperature-compensated gyroscope multi-axis motion state data to generate gyroscope multi-axis attitude drift deviation data.

[0159] In this embodiment of the invention, gyroscope multi-axis attitude drift deviation analysis is performed based on temperature-compensated gyroscope multi-axis motion state data. Real-time data of the X-axis compensated angular velocity (25.000000 degrees / s), Y-axis (30.000000 degrees / s), and Z-axis (15.000000 degrees / s) are received, and reference attitude angles (roll, pitch, and yaw) provided by a high-precision turntable (positioning accuracy ±0.001 degrees) are simultaneously input. The platform's built-in attitude calculation module calculates the actual attitude angles corresponding to the compensated data every 10ms and compares them with the reference angles: the X-axis roll angle deviation is in the range of -0.002 degrees to +0.002 degrees, with an average deviation of 0.0005 degrees; the Y-axis pitch angle deviation is in the range of -0.003 degrees to +0.003 degrees, with an average deviation of 0.0008 degrees; and the Z-axis yaw angle deviation is in the range of -0.001 degrees to +0.001 degrees, with an average deviation of 0.0003 degrees. Meanwhile, the trend of deviation over time was analyzed: the X-axis deviation exhibited slight periodic fluctuations within 1 hour (period 5s, amplitude 0.0002 degrees); the Y-axis deviation increased linearly with the extension of running time (increasing by 0.0001 degrees per hour); the Z-axis deviation remained stable. Finally, multi-axis attitude drift deviation data was generated, including the range, average value, and time trend of attitude angle deviations for each axis.

[0160] Step S52: Evaluate the attitude stability of the gyroscope based on the multi-axis attitude drift deviation data of the gyroscope, and generate gyroscope attitude stability evaluation data;

[0161] In this embodiment of the invention, the gyroscope attitude stability is evaluated based on the multi-axis attitude drift deviation data. The circuit performs statistical analysis on the X-axis roll angle deviation data. For example, if the standard deviation within one hour is 0.0006 degrees and the difference between the peak and trough values ​​is 0.004 degrees, the stability level is determined to be Level 1 (the highest level). The Y-axis pitch angle deviation has a standard deviation of 0.0009 degrees and a peak value difference of 0.006 degrees, resulting in a stability level of Level 2. The Z-axis yaw angle deviation has a standard deviation of 0.0002 degrees and a peak value difference of 0.002 degrees, resulting in a stability level of Level 1. The circuit outputs stability characteristic signals through its PWM channels (18 channels, 16-bit precision): Level 1 stability corresponds to a 20kHz pulse with a 50% duty cycle, and Level 2 corresponds to a 40% duty cycle. Simultaneously, Fourier transform analysis was used to analyze the frequency components of the deviation: the X-axis contained a small perturbation of 0.2 Hz (amplitude 0.0001 degrees), the Y-axis contained a perturbation of 0.1 Hz (amplitude 0.0002 degrees), and the Z-axis had no significant frequency components. The evaluation results showed that the stability of each axis met the preset threshold (standard deviation ≤ 0.001 degrees), with the Z-axis exhibiting the best stability, while the Y-axis required further calibration. Finally, attitude stability evaluation data including stability level, standard deviation, and frequency perturbation characteristics were generated.

[0162] Step S53: Based on the gyroscope attitude stability evaluation data, perform gyroscope multi-axis attitude and state calibration on the temperature-compensated gyroscope multi-axis motion state data to generate gyroscope multi-axis attitude-state calibration data.

[0163] In this embodiment of the invention, gyroscope attitude and state calibration is performed on temperature-compensated multi-axis motion state data based on gyroscope attitude stability evaluation data. For Y-axis level 2 stability (deviation 0.0009 degrees), the system initiates a PID calibration algorithm: proportional coefficient 0.5, integral coefficient 0.1, derivative coefficient 0.05, and correction is achieved by adjusting the servo motor drive current (range 0-500mA). When the Y-axis pitch angle deviation is +0.003 degrees, the drive current increases by 50mA to bring the deviation back to 0 degrees; when the deviation is -0.003 degrees, the current decreases by 50mA. For X-axis and Z-axis level 1 stability data, only minor corrections are made (current adjustment ±10mA). During calibration, attitude data is updated every 50ms, and the correction amount is fed back in real time via a 1553B bus (dual redundancy). After one hour of calibration, the standard deviation of the Y-axis deviation decreased to 0.0003 degrees, and the stability improved to level 1; the standard deviations of the X-axis and Z-axis deviations decreased to 0.0003 degrees and 0.0001 degrees, respectively. The final multi-axis attitude-state calibration data includes the attitude angles after calibration for each axis, the servo motor drive current, and the calibration completion time.

[0164] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the application be incorporated into the invention.

[0165] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. An online prediction and compensation method for gyroscope temperature drift, characterized in that, Includes the following steps: Step S1: Detect gyroscope operating data through sensors, identify gyroscope multi-axis motion state based on gyroscope operating data, and generate gyroscope multi-axis motion state data; Step S2: Perform multi-axis temperature disturbance torque analysis on the gyroscope based on the multi-axis motion state data of the gyroscope, and generate multi-axis temperature disturbance torque data of the gyroscope; Step S3: Collect gyroscope environmental data through sensors, and perform gyroscope temperature drift detection based on gyroscope environmental data and gyroscope multi-axis temperature disturbance torque data to generate gyroscope temperature drift data. Step S3 includes the following steps: Step S31: Collect gyroscope environmental data through sensors, extract gyroscope environmental parameter features based on the gyroscope environmental data, and generate gyroscope environmental parameter feature data; Step S32: Perform gyroscope multi-axis thermal drift mapping processing based on gyroscope environmental parameter characteristic data and gyroscope multi-axis temperature disturbance torque data to generate gyroscope multi-axis thermal drift mapping data; Step S32 includes: Step S321: Analyze the fluctuation relationship of the gyroscope's environmental factors based on the gyroscope's environmental parameter characteristic data, and generate environmental factor fluctuation relationship data; Step S322: Analyze the temperature, humidity and electromagnetic noise disturbance intensity of the gyroscope based on the environmental factor fluctuation relationship data, and generate temperature, humidity and electromagnetic noise disturbance intensity data. Step S323: Based on the temperature and humidity-electromagnetic noise disturbance intensity data, perform gyroscope axis offset and temperature trend analysis on the gyroscope multi-axis temperature disturbance torque data to generate gyroscope axis offset-temperature trend data; Step S324: Analyze the multi-axis thermal stress distribution characteristics of the gyroscope based on the offset-temperature trend data of each axis, and generate multi-axis thermal stress distribution characteristic data of the gyroscope. Step S325: Perform gyroscope multi-axis thermal drift mapping processing based on the offset-temperature trend data of each axis of the gyroscope and the multi-axis thermal stress distribution characteristic data of the gyroscope to generate gyroscope multi-axis thermal drift mapping data. Step S33: Perform gyroscope temperature drift detection based on gyroscope multi-axis thermal drift mapping data, and generate gyroscope temperature drift data; Step S4: Perform gyroscope temperature compensation processing on the gyroscope multi-axis motion state data based on the gyroscope temperature drift data to generate temperature-compensated gyroscope multi-axis motion state data. Step S5: Perform gyroscope multi-axis attitude and state calibration based on the temperature-compensated gyroscope multi-axis motion state data to generate gyroscope multi-axis attitude-state calibration data.

2. The online prediction and compensation method for gyroscope temperature drift according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Detect gyroscope operating data through sensors, identify gyroscope multi-axis motion trajectory based on gyroscope operating data, and generate gyroscope multi-axis motion trajectory data; Step S12: Analyze the multi-axis motion characteristics of the gyroscope based on the multi-axis motion trajectory data of the gyroscope, and generate multi-axis motion characteristic data of the gyroscope; Step S13: Perform gyroscope multi-axis angular velocity decomposition and motion trajectory detection based on gyroscope multi-axis motion characteristic data to generate gyroscope multi-axis angular velocity decomposition-motion trajectory data; Step S14: Based on the gyroscope multi-axis angular velocity decomposition-motion trajectory data, perform gyroscope multi-axis motion state recognition and generate gyroscope multi-axis motion state data.

3. The online prediction and compensation method for gyroscope temperature drift according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Perform gyroscope multi-axis structure partitioning analysis based on gyroscope multi-axis motion state data to generate gyroscope multi-axis structure partitioning data; Step S22: Analyze the gyroscope operation and temperature response characteristic parameters based on the gyroscope multi-axis structure partition data, and generate gyroscope operation-temperature response characteristic parameter data; Step S23: Perform multi-axis temperature coupling response analysis of the gyroscope based on the gyroscope operation-temperature response characteristic parameter data, and generate multi-axis temperature response data of the gyroscope; Step S24: Perform gyroscope multi-axis temperature disturbance torque analysis based on gyroscope multi-axis temperature response data to generate gyroscope multi-axis temperature disturbance torque data.

4. The online prediction and compensation method for gyroscope temperature drift according to claim 3, characterized in that, Step S23 includes the following steps: Step S231: Identify the multi-axis rotation nodes of the gyroscope based on the gyroscope operation-temperature response characteristic parameter data, and generate gyroscope multi-axis rotation node data; Step S232: Perform gyroscope multi-axis linkage analysis based on gyroscope multi-axis rotation node data to generate gyroscope multi-axis linkage data; Step S233: Analyze the temperature coupling strength of each axis of the gyroscope based on the multi-axis linkage data of the gyroscope, and generate temperature coupling strength data of each axis of the gyroscope. Step S234: Perform multi-axis temperature coupling response analysis of the gyroscope based on the temperature coupling strength data of each axis of the gyroscope and the multi-axis linkage data of the gyroscope, and generate multi-axis temperature response data of the gyroscope.

5. The online prediction and compensation method for gyroscope temperature drift according to claim 4, characterized in that, Step S234 includes the following steps: Based on the multi-axis linkage data of the gyroscope, the gyroscope deflection center of gravity is identified, and gyroscope deflection center of gravity data is generated. Based on the temperature coupling strength data of each axis of the gyroscope and the deflection center of gravity data of the gyroscope, the friction characteristics of the multi-bearing gyroscope are analyzed, and the friction characteristic data of the multi-bearing gyroscope are generated. Based on the temperature coupling strength data of each axis of the gyroscope, the friction characteristics data of the multi-bearing of the gyroscope, and the deflection center of gravity data of the gyroscope, the multi-axis temperature coupling response analysis of the gyroscope is performed to generate multi-axis temperature response data of the gyroscope.

6. The online prediction and compensation method for gyroscope temperature drift according to claim 3, characterized in that, Step S24 includes the following steps: Step S241: Analyze the multi-axis rotation and temperature change rate of the gyroscope based on the multi-axis temperature response data of the gyroscope, and generate multi-axis rotation-temperature change rate data of the gyroscope. Step S242: Based on the gyroscope multi-axis rotation-temperature change rate data, identify the gyroscope temperature-induced multi-axis rotation phase shift and generate gyroscope temperature-induced multi-axis rotation phase shift data; Step S243: Perform gyroscope multi-axis temperature disturbance torque analysis based on gyroscope temperature-induced multi-axis rotation phase shift data to generate gyroscope multi-axis temperature disturbance torque data.

7. The online prediction and compensation method for gyroscope temperature drift according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Perform multi-axis rotation and temperature change analysis of the gyroscope based on the gyroscope temperature drift data to generate multi-axis rotation-temperature change data of the gyroscope; Step S42: Based on the multi-axis rotation-temperature change data of the gyroscope, identify the multi-axis temperature-induced dynamic imbalance characteristics of the gyroscope based on the multi-axis motion state data of the gyroscope, and generate multi-axis temperature-induced dynamic imbalance characteristic data of the gyroscope. Step S43: Based on the multi-axis temperature-induced dynamic imbalance characteristic data of the gyroscope, perform gyroscope temperature drift influence area location analysis and generate gyroscope temperature drift influence area location data; Step S44: Perform gyroscope temperature compensation processing based on the gyroscope temperature drift affected area positioning data and gyroscope multi-axis rotation-temperature change data to generate temperature-compensated gyroscope multi-axis motion state data.

8. The online prediction and compensation method for gyroscope temperature drift according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Perform gyroscope multi-axis attitude drift deviation analysis based on the temperature-compensated gyroscope multi-axis motion state data to generate gyroscope multi-axis attitude drift deviation data. Step S52: Evaluate the attitude stability of the gyroscope based on the multi-axis attitude drift deviation data of the gyroscope, and generate gyroscope attitude stability evaluation data; Step S53: Based on the gyroscope attitude stability evaluation data, perform gyroscope multi-axis attitude and state calibration on the temperature-compensated gyroscope multi-axis motion state data to generate gyroscope multi-axis attitude-state calibration data.

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